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Catalyze and enable your data use cases.

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  1. Liberate

    Disparate, non-standard, real-world data is collected from varied source systems (e.g., payer, provider, consumer, SDOH), formats, and transports (e.g., HL7, CCDA, FHIR, CCLF, EHR).

  2. Aggregate

    Data is aggregated according to unique individuals, providers, and clinical concepts. Identity privacy-preserving record linking ensures records belonging to the same individual from different sources are consistently linked together.

  3. Organize

    Data is stored in a consistent patient-centric model, regardless of source or data type. Source data provenance is maintained in its original form for traceability.

  4. Standardize

    Data is harmonized and standardized automatically, ensuring dirty, uncoded concepts are mapped to standard codesystems (e.g., ICD-10, RxNORM, SNOMED). Once coded, concepts are mapped across multiple terminology standards (e.g., ICD-10 to SNOMED, NDC to RxNORM).

  5. Group

    Similar codes are grouped together (e.g., therapeutic classes, drug ingredients, HCC categories, AHRQ value sets) to make data analytically ready.

  6. Enrich & Transform

    Unstructured data is stacked, normalized, and transformed into APIs and data pipelines (e.g., Tableau, SQL, Jupyter, Amazon Redshift, Snowflake, FHIR, OMOP) to feed varied applications and use cases.

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No data left behind

Raw data from various sources is loaded via Orchestrate and aggregated according to unique individuals, providers, and clinical concepts.

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Creating the lifetime person record

Data is cleaned and standardized automatically, ensuring patient information is consistently tracked across data sources with similar codes grouped together to make data analytically ready.

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Enabling the digital health ecosystem

Analytically-ready data enables interoperability solutions, digital clinical trials, and extensive other use cases.

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Flexible architecture

Build your own solutions via a set of robust self-service APIs. Or take advantage of the cloud-native, full-stack Orchestrate platform.

From ingestion and aggregation of raw data, to standardization and harmonization of disparate data types

For developers

Author healthcare applications, from consumer apps to population health analytics, without worrying about inconsistency or variance among data assets.

  • Terminology

    Make use of standard code systems and value sets to map and classify inputs to common references.

  • Convert

    Transform your input data from one standardized format to another. This includes parsing the output, organizing it into the appropriate data types, standardizing source codings as appropriate, and outputting it in another format.

  • Insight

    Compute advanced derived data elements, such as condition profiles, risk strata, and gaps in care, from FHIR bundles processed via Convert.

  • Identity

    Integrate world-class patient matching into your solution, enabling privacy-preserving (blinded) patient matching across a spectrum of data sources and use cases.

  • Identity Lite

    Securely match patient records in 2 simple steps.